Breast Ultra-Sound image segmentation: an optimization approach based on super-pixels and high-level descriptors

Joan Massich 1 Guillaume Lemaitre 1, 2 Joan Martí 2 Fabrice Mériaudeau 1
1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6303]
2 VICOROB - Research institute of Computer Vision and Robotics [Girona]
Abstract : Breast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the breast are essential for CAD systems in order to extract information needed to perform diagnosis. This article proposes a highly modular and flexible framework for segmenting lesions and tissues present in BUS images. The proposal takes advantage of optimization strategies using super-pixels and high-level de-scriptors, which are analogous to the visual cues used by radiologists. Qualitative and quantitative results are provided stating a performance within the range of the state-of-the-art.
Keywords : Breast Ultra-Sound BI-RADS lexicon Optimization based Segmentation Machine-Learning based Segmentation Graph-Cuts
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Dernière modification le : mercredi 12 septembre 2018 - 01:26:33
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Joan Massich, Guillaume Lemaitre, Joan Martí, Fabrice Mériaudeau. Breast Ultra-Sound image segmentation: an optimization approach based on super-pixels and high-level descriptors. International Conference on Quality Control and Artificial Vision (QCAV) 2015, Jun 2015, Le Creusot, France. ⟨hal-01235889⟩

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